ReforesTree: A Dataset for Estimating Tropical Forest Carbon Stock with Deep Learning and Aerial Imagery
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）；University of Warwick（华威大学）；New York University（纽约大学）；WWF Switzerland（世界自然基金会瑞士分会）
Gyri Reiersen, David Dao, Björn Lütjens, Konstantin Klemmer, Kenza Amara, Attila Steinegger, Ce Zhang, Xiaoxiang Zhu
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Accepted paper for the AI for Social Impact Track at the AAAI 2022
Forest biomass is a key influence for future climate, and the world urgently needs highly scalable financing schemes, such as carbon offsetting certifications, to protect and restore forests. Current manual forest carbon stock inventory methods of measuring single trees by hand are time, labour, and cost-intensive and have been shown to be subjective. They can lead to substantial overestimation of the carbon stock and ultimately distrust in forest financing. The potential for impact and scale of leveraging advancements in machine learning and remote sensing technologies is promising but needs to be of high quality in order to replace the current forest stock protocols for certifications.
In this paper, we present ReforesTree, a benchmark dataset of forest carbon stock in six agro-forestry carbon offsetting sites in Ecuador. Furthermore, we show that a deep learning-based end-to-end model using individual tree detection from low cost RGB-only drone imagery is accurately estimating forest carbon stock within official carbon offsetting certification standards. Additionally, our baseline CNN model outperforms state-of-the-art satellite-based forest biomass and carbon stock estimates for this type of small-scale, tropical agro-forestry sites. We present this dataset to encourage machine learning research in this area to increase accountability and transparency of monitoring, verification and reporting (MVR) in carbon offsetting projects, as well as scaling global reforestation financing through accurate remote sensing.
SelvaBox: A high-resolution dataset for tropical tree crown detection
Mila – Quebec AI Institute（魁北克人工智能研究所）；Université de Montréal（蒙特利尔大学）；McGill University（麦吉尔大学）；Rubisco AI（Rubisco人工智能）；Colorado Mesa University（科罗拉多 mesa大学）；Polytechnique Montreal（蒙特利尔理工学院）
University of Cambridge（剑桥大学）；Royal Society for Protection of Birds（英国皇家鸟类保护协会）；Jambi University（占碑大学）；PT Restorasi Ekosistem Indonesia（印度尼西亚生态系统恢复公司）
Logged forests cover four million square kilometres of the tropics and restoring these forests is essential if we are to avoid the worst impacts of climate change, yet monitoring recovery is challenging. Tracking the abundance of visually identifiable, early-successional species enables successional status and thereby restoration progress to be evaluated. Here we present a new pipeline, SLIC-UAV, for processing Unmanned Aerial Vehicle (UAV) imagery to map early-successional species in tropical forests. The pipeline is novel because it comprises: (a) a time-efficient approach for labelling crow…
Real-Time Localization and Bimodal Point Pattern Analysis of Palms Using UAV Imagery
City University of Hong Kong（香港城市大学）；Hong Kong Centre for Cerebro-Cardiovascular Health Engineering（香港脑心血管健康工程中心）；Xidian University（西安电子科技大学）；Wake Forest University（维克森林大学）；University of California, Berkeley（加州大学伯克利分校）；Tulane University（杜兰大学）；Lingnan University（岭南大学）
Understanding Representation Gaps Across Scales in Tropical Tree Species Classification from Drone Imagery
Mila – Quebec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；Université de Montréal（蒙特利尔大学）；Cary Institute of Ecosystem Studies（生态系统研究所）；Smithsonian Tropical Research Institute（史密松国家热带研究所）；Department of Plant Sciences, University of Cambridge（剑桥大学植物科学系）；Universidade do Estado do Mato Grosso (UNEMAT)（马托格罗索州立大学（UNEMAT））
deadtrees.earth-aerial: A Multi-Resolution Aerial Image Dataset for Tree Cover and Mortality Detection
Chair of Sensor-based Geoinformatics, University of Freiburg（传感器基于地理信息学系，弗赖堡大学）；EcoVision Lab, DM3L, University of Zurich（生态视觉实验室，苏黎世大学）；Institute for Earth System Science and Remote Sensing, Leipzig University（地球系统科学与遥感研究所，莱比锡大学）；Washington University, St. Louis（斯蒂芬斯敦大学）
Technical University of Munich（慕尼黑工业大学）；ETH Zurich（苏黎世联邦理工学院）；Massachusetts Institute of Technology（麻省理工学院）
Forest carbon offsets are increasingly popular and can play a significant role in financing climate mitigation, forest conservation, and reforestation. Measuring how much carbon is stored in forests is, however, still largely done via expensive, time-consuming, and sometimes unaccountable field measurements. To overcome these limitations, many verification bodies are leveraging machine learning (ML) algorithms to estimate forest carbon from satellite or aerial imagery. Aerial imagery allows for tree species or family classification, which improves the satellite imagery-based forest type classi…
The increasing demand for commodities is leading to changes in land use worldwide. In the tropics, deforestation, which causes high carbon emissions and threatens biodiversity, is often linked to agricultural expansion. While the need for deforestation-free global supply chains is widely recognized, making progress in practice remains a challenge. Here, we propose an automated approach that aims to support conservation and sustainable land use planning decisions by mapping tropical landscapes at large scale and high spatial resolution following the High Carbon Stock (HCS) approach. A deep lear…
University of California, Los Angeles（加州大学洛杉矶分校）；California Institute of Technology（加州理工学院）；NASA-Jet Propulsion Laboratory（美国国家航空航天局喷气推进实验室）；Virginia Polytechnic Institute and State University（弗吉尼亚理工学院暨州立大学）
Monitoring changes in tree cover for rapid assessment of deforestation is considered the critical component of any climate mitigation policy for reducing carbon. Here, we map tropical tree cover and deforestation between 2015 and 2022 using 5 m spatial resolution Planet NICFI satellite images over the state of Mato Grosso (MT) in Brazil and a U-net deep learning model. The tree cover for the state was 556510.8 km$^2$ in 2015 (58.1 % of the MT State) and was reduced to 141598.5 km$^2$ (14.8 % of total area) at the end of 2021. After reaching a minimum deforested area in December 2016 with 6632.…
Highly Detailed and Generalizable Broadleaf Tree Crown Instance Segmentation from UAV Imagery
DeepForest Technologies Co., Ltd.（深森林技术有限公司）；YM Lab.（YM实验室）；Graduate School of Agriculture, Kyoto University（京都大学农业研究院）；Graduate School of Science, Osaka Metropolitan University（大阪 metropolitan 大学理学研究院）；Faculty of Tropical Forestry, Universiti Malaysia Sabah（马来西亚沙巴大学热带林业学院）；Forest Research Centre, Sabah Forestry Department（沙巴林业部门森林研究中心）
Phase-Preserving Trimodal Transformer for Tropical Forest Biomass Estimation Using Optical and PolInSAR Data
Institute of Computing (IComp), Federal University of Amazonas (UFAM)（亚马逊联邦大学计算研究所）；Brazilian Army Research Institute in the Amazon (IPEAM)（巴西陆军亚马逊研究所）；Military Institute of Engineering (IME)（军事工程学院）
Texas A&M University（德克萨斯农工大学）；Mississippi State University（密西西比州立大学）；U.S.D.A. Agriculture Research Service（美国农业部农业研究局）；U.S.D.A.- APHIS PPQ S&T（美国农业部动植物卫生检验局植物保护与检疫科学技术处）；Utah State University（犹他州立大学）；Texas A&M AgriLife Research & Extension Center（德克萨斯农工大学生命科学研究与推广中心）
The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 l…
